Sleep-dependent Neurophysiological Processes in Implicit Sequence Learning (original) (raw)
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Sleep has no critical role in implicit motor sequence learning in young and old adults
Experimental Brain Research, 2010
The inXuence of sleep on motor skill consolidation has been a research topic of increasing interest. In this study, we distinguished general skill learning from sequence-speciWc learning in a probabilistic implicit sequence learning task (alternating serial reaction time) in young and old adults before and after a 12-h oZine interval which did or did not contain sleep (p.m.-a.m. and a.m.-p.m. groups, respectively). The results showed that general skill learning, as assessed via overall reaction time, improved oZine in both the young and older groups, with the young group improving more than the old. However, the improvement was not sleep-dependent, in that there was no diVerence between the a.m.-p.m. and p.m.-a.m. groups. We did not Wnd sequence-speciWc oZine improvement in either age group for the a.m.-either p.m. or p.m.-a.m. groups, suggesting that consolidation of this kind of implicit motor sequence learning may not be inXuenced by sleep.
Implicit structured sequence learning: an fMRI study of the structural mere-exposure effect
Frontiers in Psychology, 2014
In this event-related fMRI study we investigated the effect of 5 days of implicit acquisition on preference classification by means of an artificial grammar learning (AGL) paradigm based on the structural mere-exposure effect and preference classification using a simple right-linear unification grammar. This allowed us to investigate implicit AGL in a proper learning design by including baseline measurements prior to grammar exposure. After 5 days of implicit acquisition, the fMRI results showed activations in a network of brain regions including the inferior frontal (centered on BA 44/45) and the medial prefrontal regions (centered on BA 8/32). Importantly, and central to this study, the inclusion of a naive preference fMRI baseline measurement allowed us to conclude that these fMRI findings were the intrinsic outcomes of the learning process itself and not a reflection of a preexisting functionality recruited during classification, independent of acquisition. Support for the implicit nature of the knowledge utilized during preference classification on day 5 come from the fact that the basal ganglia, associated with implicit procedural learning, were activated during classification, while the medial temporal lobe system, associated with explicit declarative memory, was consistently deactivated. Thus, preference classification in combination with structural mere-exposure can be used to investigate structural sequence processing (syntax) in unsupervised AGL paradigms with proper learning designs. . Citation: Folia V and Petersson KM (2014) Implicit structured sequence learning: an fMRI study of the structural mere-exposure effect. Front. Psychol. 5:41.
PLoS biology, 2016
Sleep plays a role in memory consolidation. This is demonstrated by improved performance and neural plasticity underlying that improvement after sleep. Targeted memory reactivation (TMR) allows the manipulation of sleep-dependent consolidation through intentionally biasing the replay of specific memories in sleep, but the underlying neural basis of these altered memories remains unclear. We use functional magnetic resonance imaging (fMRI) to show a change in the neural representation of a motor memory after targeted reactivation in slow-wave sleep (SWS). Participants learned two serial reaction time task (SRTT) sequences associated with different auditory tones (high or low pitch). During subsequent SWS, one sequence was reactivated by replaying the associated tones. Participants were retested on both sequences the following day during fMRI. As predicted, they showed faster reaction times for the cued sequence after targeted memory reactivation. Furthermore, increased activity in bi...
Consolidating the Effects of Waking and Sleep on Motor-Sequence Learning
Journal of Neuroscience, 2010
Sleep is widely believed to play a critical role in memory consolidation. Sleep-dependent consolidation has been studied extensively in humans using an explicit motor-sequence learning paradigm. In this task, performance has been reported to remain stable across wakefulness and improve significantly after sleep, making motor-sequence learning the definitive example of sleep-dependent enhancement. Recent work, however, has shown that enhancement disappears when the task is modified to reduce task-related inhibition that develops over a training session, thus questioning whether sleep actively consolidates motor learning. Here we use the same motorsequence task to demonstrate sleep-dependent consolidation for motor-sequence learning and explain the discrepancies in results across studies. We show that when training begins in the morning, motor-sequence performance deteriorates across wakefulness and recovers after sleep, whereas performance remains stable across both sleep and subsequent waking with evening training. This pattern of results challenges an influential model of memory consolidation defined by a time-dependent stabilization phase and a sleep-dependent enhancement phase. Moreover, the present results support a new account of the behavioral effects of waking and sleep on explicit motor-sequence learning that is consistent across a wide range of tasks. These observations indicate that current theories of memory consolidation that have been formulated to explain sleep-dependent performance enhancements are insufficient to explain the range of behavioral changes associated with sleep.
Sleep transforms the cerebral trace of declarative memories
Proceedings of The National Academy of Sciences, 2007
After encoding, memory traces are initially fragile and have to be reinforced to become permanent. The initial steps of this process occur at a cellular level within minutes or hours. Besides this rapid synaptic consolidation, systems consolidation occurs within a time frame of days to years. For declarative memory, the latter is presumed to rely on an interaction between different brain regions, in particular the hippocampus and the medial prefrontal cortex (mPFC). Specifically, sleep has been proposed to provide a setting that supports such systems consolidation processes, leading to a transfer and perhaps transformation of memories. Using functional MRI, we show that postlearning sleep enhances hippocampal responses during recall of word pairs 48 h after learning, indicating intrahippocampal memory processing during sleep. At the same time, sleep induces a memory-related functional connectivity between the hippocampus and the mPFC. Six months after learning, memories activated the mPFC more strongly when they were encoded before sleep, showing that sleep leads to longlasting changes in the representation of memories on a systems level.
Brain and cognition, 2015
That post-training sleep supports the consolidation of sequential motor skills remains debated. Performance improvement and sensitivity to proactive interference are both putative measures of long-term memory consolidation. We tested sleep-dependent memory consolidation for visuo-motor sequence learning using a proactive interference paradigm. Thirty-three young adults were trained on sequence A on Day 1, then had Regular Sleep (RS) or were Sleep Deprived (SD) on the night after learning. After two recovery nights, they were tested on the same sequence A, then had to learn a novel, potentially competing sequence B. We hypothesized that proactive interference effects on sequence B due to the prior learning of sequence A would be higher in the RS condition, considering that proactive interference is an indirect marker of the robustness of sequence A, which should be better consolidated over post-training sleep. Results highlighted sleep-dependent improvement for sequence A, with faste...
An fMRI Study of the Role of the Medial Temporal Lobe in Implicit and Explicit Sequence Learning
Neuron, 2003
and Brain longer-term, explicit episodic retrieval of longer se-Department of Psychology quences, memory accounts implicate the MTL (Squire Boston University and Zola-Morgan, 1991). For implicit sequence learning, Boston, Massachusetts 02215 both frameworks implicate the striatum. While motor 2 MGH-NMR Center models also include the supplementary motor area Department of Radiology (SMA), parietal lobe, and cerebellum (Willingham, 1998; Harvard Medical School Middleton and Strick, 2000), some memory accounts Charlestown, Massachusetts 02129 implicate MTL structures in certain types of implicit learning (Curran, 1997; Cohen and Eichenbaum, 1993). We used fMRI to investigate the role of the human Summary MTL in implicit and explicit sequence learning. fMRI data were acquired while subjects performed a serial reaction fMRI was used to investigate the neural substrates time task (SRTT; Figure 1A), developed originally by Nissupporting implicit and explicit sequence learning, fosen and Bullemer (1987). In the SRTT, learning results in cusing especially upon the role of the medial temporal faster response times (RTs) for repeated than for random lobe. Participants performed a serial reaction time sequences of cued locations. task (SRTT). For implicit learning, they were naive For implicit SRTT learning, convergent evidence impliabout a repeating pattern, whereas for explicit learncates subcortical and cortical components of frontostriing, participants memorized another repeating seatal pathways. Patients with striatal dysfunction are imquence. fMRI analyses comparing repeating versus paired on implicit SRTTs (Knopman and Nissen, 1991; random sequence blocks demonstrated activation of Vakil et al., 2000; Jackson et al., 1995; Doyon et al., 1997, frontal, parietal, cingulate, and striatal regions impli-1998). Neuroimaging studies using an implicit SRTT with cated in previous SRTT studies. Importantly, mediohealthy adults have shown activation in the caudate, temporal lobe regions were active in both explicit and putamen (Rauch et al., 1995, 1997a; Hazeltine et al., implicit SRTT learning. Moreover, the results provide 1997; Grafton et al., 1995; Willingham et al., 2002; Peigevidence of a role for the hippocampus and related neux et al., 2000) and ventral striatum (Berns et al., 1997; cortices in the formation of higher order associations Doyon et al., 1996). The caudate has been proposed to under both implicit and explicit learning conditions, be important for stimulus-response association (Polregardless of conscious awareness of sequence drack et al., 2001) and cognitive abilities, such as workknowledge. ing memory (Owen et al., 1998). Activation has also been found in cortical components of frontostriatal circuits, Introduction including the DLPFC, parietal lobe, premotor cortex, anterior cingulate, and SMA (Rauch et al., 1995, 1997b; Sequence learning is used for behaviors like typing, mu-Berns et al., 1997; Grafton et al., 1995, 1998; Hazeltine sical performance, and route navigation. Researchers et al., 1997; Willingham et al., 2002; Peigneux et al., have described the acquisition of perceptuomotor se-2000). quencing skills using either motor control (e.g., Hazeltine For explicit SRTT learning, while striatal activation has et al., 1997) or learning and memory frameworks (e.g., rarely been noted, neuroimaging studies consistently Reber and Squire, 1998). Both explanations agree that find activation in cortical components of frontostriatal distinct brain processes support explicit learning, which circuits, including the DLPFC, ventrolateral prefrontal, occurs with awareness, and implicit learning, which ocpremotor, anterior cingulate, and dorsal and inferior pacurs without awareness. However, the two accounts rietal cortices (Hazeltine et al., 1997; Jenkins et al., 1994; diverge over which brain systems are important. Most Grafton et al., 1995; Rauch et al., 1995; Willingham et critically, only memory frameworks posit a role for the al., 2002). Motor accounts posit the DLPFC controls mediotemporal lobe (MTL) in sequence learning. strategic processes throughout the explicit SRTT, and For explicit sequence learning, both motor control and it can be recruited during an implicit SRTT, if participants memory accounts implicate the dorsolateral prefrontal become aware of a sequence (Willingham, 1998). This cortex (DLPFC). This region, by motor accounts, supidea resembles an explicit-implicit variety of memory ports conscious executive motor control to select goals account but with the DLPFC, not MTL, being necessary or to select and maintain a spatial sequence in working for conscious sequence acquisition. memory (Willingham, 1998; Grafton et al., 1995; Ha-Memory, but not motor, accounts consider the MTL zeltine et al., 1997) or, by memory accounts, supports system to be necessary for learning sequences, espethe manipulation and monitoring functions of working cially those beyond the capacity of DLPFC processes memory (Smith and Jonides, 1999). Memory models, of working memory. Each account, however, posits a however, also posit that the role of the DLPFC is limited somewhat different role for MTL. For explicit learning, explicit-implicit (or declarative-nondeclarative) memory accounts state that the MTL is necessary for long-term
Cued memory reactivation during slow-wave sleep promotes explicit knowledge of a motor sequence
The Journal of neuroscience : the official journal of the Society for Neuroscience, 2014
Memories are gradually consolidated after initial encoding, and this can sometimes lead to a transition from implicit to explicit knowledge. The exact physiological processes underlying this reorganization remain unclear. Here, we used a serial reaction time task to determine whether targeted memory reactivation (TMR) of specific memory traces during slow-wave sleep promotes the emergence of explicit knowledge. Human participants learned two 12-item sequences of button presses (A and B). These differed in both cue order and in the auditory tones associated with each of the four fingers (one sequence had four higher-pitched tones). Subsequent overnight sleep was monitored, and the tones associated with one learned sequence were replayed during slow-wave sleep. After waking, participants demonstrated greater explicit knowledge (p = 0.005) and more improved procedural skill (p = 0.04) for the cued sequence relative to the uncued sequence. Furthermore, fast spindles (13.5-15 Hz) at task...